def test_rbm(learning_rate=0.1,
training_epochs=15,
dataset='mnist.pkl.gz',
batch_size=20,
n_chains=20,
n_samples=10,
output_folder='rbm_plots',
n_hidden=500):
"""
Demonstrate how to train and afterwards sample from it using Theano.
This is demonstrated on MNIST.
:param learning_rate: learning rate used for training the RBM
:param training_epochs: number of epochs used for training
:param dataset: path the the pickled dataset
:param batch_size: size of a batch used to train the RBM
:param n_chains: number of parallel Gibbs chains to be used for sampling
:param n_samples: number of samples to plot for each chain
"""
datasets = load_dataset(dataset)
train_set_x, train_set_y = datasets[0]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2**30))
# initialize storage for the persistent chain (state = hidden
# layer of chain)
persistent_chain = theano.shared(numpy.zeros((batch_size, n_hidden),
dtype=theano.config.floatX),
borrow=True)
print '...... building model ......'
# construct the RBM class
rbm = RBM(input=x,
n_visible=28 * 28,
n_hidden=n_hidden,
numpy_rng=rng,
theano_rng=theano_rng)
# get the cost and the gradient corresponding to one step of CD-15
cost, updates = rbm.get_cost_updates(lr=learning_rate,
persistent=persistent_chain,
k=15)
#################################
# Training the RBM #
#################################
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
# start-snippet-5
# it is ok for a theano function to have no output
# the purpose of train_rbm is solely to update the RBM parameters
train_rbm = theano.function(
[index],
cost,
updates=updates,
givens={x: train_set_x[index * batch_size:(index + 1) * batch_size]},
name='train_rbm')
print '...... training ......'
plotting_time = 0.
start_time = timeit.default_timer()
# go through training epochs
for epoch in range(training_epochs):
# go through the training set
mean_cost = []
for batch_index in range(n_train_batches):
mean_cost += [train_rbm(batch_index)]
print('Training epoch %d, cost is ' % epoch, numpy.mean(mean_cost))
# Plot filters after each training epoch
plotting_start = timeit.default_timer()
# Construct image from the weight matrix
image = Image.fromarray(
tile_raster_images(X=rbm.W.get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('filters_at_epoch_%i.png' % epoch)
plotting_stop = timeit.default_timer()
plotting_time += (plotting_stop - plotting_start)
end_time = timeit.default_timer()
pretraining_time = (end_time - start_time) - plotting_time
print('Training took %f minutes' % (pretraining_time / 60.))
# end-snippet-5 start-snippet-6
#################################
# Sampling from the RBM #
#################################
# find out the number of test samples
number_of_test_samples = test_set_x.get_value(borrow=True).shape[0]
# pick random test examples, with which to initialize the persistent chain
test_idx = rng.randint(number_of_test_samples - n_chains)
persistent_vis_chain = theano.shared(
numpy.asarray(test_set_x.get_value(borrow=True)[test_idx:test_idx +
n_chains],
dtype=theano.config.floatX))
# end-snippet-6 start-snippet-7
plot_every = 1000
# define one step of Gibbs sampling (mf = mean-field) define a
# function that does `plot_every` steps before returning the
# sample for plotting
([presig_hids, hid_mfs, hid_samples, presig_vis, vis_mfs,
vis_samples], updates) = theano.scan(
rbm.gibbs_vhv,
outputs_info=[None, None, None, None, None, persistent_vis_chain],
n_steps=plot_every,
name="gibbs_vhv")
# add to updates the shared variable that takes care of our persistent
# chain :.
updates.update({persistent_vis_chain: vis_samples[-1]})
# construct the function that implements our persistent chain.
# we generate the "mean field" activations for plotting and the actual
# samples for reinitializing the state of our persistent chain
sample_fn = theano.function([], [vis_mfs[-1], vis_samples[-1]],
updates=updates,
name='sample_fn')
# create a space to store the image for plotting ( we need to leave
# room for the tile_spacing as well)
image_data = numpy.zeros((29 * n_samples + 1, 29 * n_chains - 1),
dtype='uint8')
for idx in range(n_samples):
# generate `plot_every` intermediate samples that we discard,
# because successive samples in the chain are too correlated
vis_mf, vis_sample = sample_fn()
print(' ... plotting sample %d' % idx)
image_data[29 * idx:29 * idx + 28, :] = tile_raster_images(
X=vis_mf,
img_shape=(28, 28),
tile_shape=(1, n_chains),
tile_spacing=(1, 1))
# construct image
image = Image.fromarray(image_data)
image.save('samples.png')
# end-snippet-7
os.chdir('../')
def test_sdA(finetune_lr=0.1,
pretraining_epochs=15,
pretrain_lr=0.001,
training_epochs=1000,
dataset='mnist.pkl.gz',
batch_size=1):
datasets = load_dataset(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_train_batches //= batch_size
numpy_rng = numpy.random.RandomState(89677)
print '...... building model ......'
"""sdA
def __init__(self,
numpy_rng,theano_rng=None,n_ins=784,hidden_layer_sizes=[500,500],
n_out=10,corruption_levels=[0.1,0.1]):
"""
sda = sdA(numpy_rng=numpy_rng,
n_ins=28 * 28,
hidden_layer_sizes=[1000, 1000, 1000],
corruption_levels=[0.1, 0.1, 0.1],
n_out=10)
#==============================================================================
#============首先进行无监督学习，即根据给定的隐藏层节点个数构建自编码网络================
#==============================================================================
print('...... getting the pretraining functions ......')
pretraining_fns = sda.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size)
print('...... pre-training the model......')
start_time = timeit.default_timer()
corruption_levels = [.1, .2, .3]
for i in range(sda.n_layers):
for epoch in range(pretraining_epochs):
c = []
for batch_index in range(n_train_batches):
c.append(pretraining_fns[i](index=batch_index,
corruption=corruption_levels[i],
lr=pretrain_lr))
print('Pre-training layer %i, epoch %d, cost %f' %
(i, epoch, numpy.mean(c)))
end_time = timeit.default_timer()
print >> sys.stderr, ('The pretraining code for file ' +
os.path.split(__file__)[1] + ' ran for %.1fs' %
(end_time - start_time))
#==============================================================================
#========进行监督学习，根据创建好的自编码网络，结合MLP和Softmax回归训练网络参数==========
#==============================================================================
print('...... getting the finetuning functions .......')
train_fn, validate_model, test_model = sda.build_finetune_functions(
datasets=datasets, batch_size=batch_size, learning_rate=finetune_lr)
print('...... finetunning the model.......')
patience = 10 * n_train_batches # look as this many examples regardless
patience_increase = 2.
improvement_threshold = 0.995
validation_frequency = min(n_train_batches, patience // 2)
best_validation_loss = numpy.inf
test_score = 0.
start_time = timeit.default_timer()
done_looping = False
epoch = 0
while (epoch < training_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = validate_model()
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if this_validation_loss < best_validation_loss:
if (this_validation_loss <
best_validation_loss * improvement_threshold):
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = test_model()
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
#==============================================================================
#======================All Done================================================
print(('Optimization complete with best validation score of %f %%, '
'on iteration %i, '
'with test performance %f %%') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The training code for file ' +
os.path.split(__file__)[1] + ' ran for %.1fs' %
(end_time - start_time))
def test_DA(learning_rate=0.1,
training_epochs=15,
dataset='mnist.pkl.gz',
batch_size=20,
output_folder='DA_plots'):
datasets = load_dataset(dataset)
train_set_x, train_set_y = datasets[0]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
index = T.lscalar()
x = T.matrix('x')
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
#==============================================================================
#================================Autoencoders =================================
print '...... building corruption = 0.0 model ......'
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2**30))
da = dA(numpy_rng=rng,
theano_rng=theano_rng,
input=x,
n_hidden=500,
n_visible=28 * 28)
cost, updates = da.get_cost_update(corruption_level=0.,
learning_rate=learning_rate)
train_da = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={x: train_set_x[index * batch_size:(index + 1) * batch_size]})
print '...... training corruption = 0.0 ......'
start_time = timeit.default_timer()
for epoch in range(training_epochs):
c = []
for batch_index in range(n_train_batches):
c.append(train_da(batch_index))
print 'Training epoch %d,cost = ' % epoch, numpy.mean(c)
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print "###################"
print da.W.shape
print >> sys.stderr, ('The 0% corruption code for file ' +
os.path.split(__file__)[1] + ' ran for %.1fs' %
(training_time))
image = Image.fromarray(
tile_raster_images(X=da.W.get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=(20, 20),
tile_spacing=(1, 1)))
image.save('filters_corruption_0.png')
#==============================================================================
#========================Denoising Autoencoder=================================
print '...... building corruption = 0.3 model ......'
rng = numpy.random.RandomState(123)
theano_rng = RandomStreams(rng.randint(2**30))
da = dA(numpy_rng=rng,
theano_rng=theano_rng,
input=x,
n_visible=28 * 28,
n_hidden=500)
cost, updates = da.get_cost_update(corruption_level=0.3,
learning_rate=learning_rate)
train_da = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={x: train_set_x[index * batch_size:(index + 1) * batch_size]})
print '...... training corruption = 0.0 ......'
start_time = timeit.default_timer()
for epoch in range(training_epochs):
c = []
for batch_index in range(n_train_batches):
c.append(train_da(batch_index))
print('Training epoch %d, cost ' % epoch, numpy.mean(c))
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print >> sys.stderr, ('The 30% corruption code for file ' +
os.path.split(__file__)[1] + ' ran for %.1fs' %
(training_time))
image = Image.fromarray(
tile_raster_images(X=da.W.get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=(20, 20),
tile_spacing=(1, 1)))
image.save('filters_corruption_30.png')
os.chdir('../')
def test_DBN(finetune_lr=0.1,
pretraining_epochs=100,
pretrain_lr=0.01,
k=1,
training_epochs=1000,
dataset='mnist.pkl.gz',
batch_size=10):
datasets = load_dataset(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
numpy_rng = numpy.random.RandomState(123)
print('...... building the model ......')
# construct the Deep Belief Network
dbn = DBN(numpy_rng=numpy_rng,
n_ins=28 * 28,
hidden_layers_sizes=[1000, 1000, 1000],
n_outs=10)
#======================Pretraining the model ==================================
print('...... getting the pretraining functions ......')
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x,
batch_size=batch_size,
k=k)
print('...... pre-training the model ......')
start_time = timeit.default_timer()
for i in range(dbn.n_layers):
for epoch in range(pretraining_epochs):
c = []
for batch_index in range(n_train_batches):
c.append(pretraining_fns[i](index=batch_index, lr=pretrain_lr))
print('Pre-training layer {0}, epoch {1}, cost {2}'.format(
i, epoch, numpy.mean(c)))
end_time = timeit.default_timer()
print('The pretraining code for file {0} ran for {1}m'.format(
os.path.split(__file__)[1], ((end_time - start_time) / 60.)))
#======================FineTuning the model ===================================
print('...... getting the finetuning functions ......')
train_fn, validate_model, test_model = dbn.build_finetune_functions(
datasets=datasets, batch_size=batch_size, learning_rate=finetune_lr)
print('...... finetuning the model ......')
patience = 4 * n_train_batches
patience_increase = 2.
improvement_threshold = 0.995
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = numpy.inf
test_score = 0.
start_time = timeit.default_timer()
done_looping = False
epoch = 0
while (epoch < training_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = validate_model()
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if this_validation_loss < best_validation_loss:
if (this_validation_loss <
best_validation_loss * improvement_threshold):
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = test_model()
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print(('Optimization complete with best validation score of %f %%, '
'obtained at iteration %i, '
'with test performance %f %%') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print('The fine tuning code for file {0} ran for {1}m'.format(
os.path.split(__file__)[1], ((end_time - start_time) / 60.)))
def evaluate_lenet5(learning_rate=0.1,
n_epochs=200,
dataset='mnist.pkl.gz',
nkerns=[20, 50],
batch_size=500):
rng = numpy.random.RandomState(23455)
datasets = load_dataset(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches //= batch_size
n_valid_batches //= batch_size
n_test_batches //= batch_size
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
print '...... building the model ......'
layer0_input = x.reshape((batch_size, 1, 28, 28))
layer0 = LeNetConvPoolLayer(rng=rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2))
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
layer2_input = layer1.output.flatten(2)
layer2 = HiddenLayer(rng=rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh)
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
cost = layer3.negative_log_likelihood(y)
test_model = theano.function(
[index],
layer3.error(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
[index],
layer3.error(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
params = layer3.params + layer2.params + layer1.params + layer0.params
grads = T.grad(cost, params)
updates = [(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)]
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
print '...... training ......'
patience = 10000
patience_increase = 2
improvement_threshold = 0.995
validation_frequency = min(n_train_batches, patience // 2)
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print('training @ iter = ', iter)
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
validation_losses = [
validate_model(i) for i in range(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if this_validation_loss < best_validation_loss:
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [
test_model(i) for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print('Optimization complete.')
print(
'Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time)))
def test_mlp(learning_rate=0.01,
L1_reg=0.00,
L2_reg=0.0001,
n_epochs=10,
dataset='mnist.pkl.gz',
batch_size=20,
n_hidden=500):
datasets = lr.load_dataset(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
print '...... building model ......'
index = T.iscalar()
x = T.matrix("x")
y = T.ivector("y")
rng = np.random.RandomState(1234)
classifier = MLP(rng=rng,
input=x,
n_in=28 * 28,
n_hidden=n_hidden,
n_out=10)
cost = (classifier.negative_log_likelihood(y) + L1_reg * classifier.L1 +
L2_reg * classifier.L2_sqr)
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
gparams = [T.grad(cost, param) for param in classifier.params]
#zip函数接受任意多个（包括0个和1个）序列作为参数，返回一个tuple列表
updates = [(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, gparams)]
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
patience = 10000
patience_increase = 2
improvement_threshold = 0.995
validation_frequency = min(n_train_batches, patience // 2)
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
print '...... training model ......'
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = [
validate_model(i) for i in range(n_valid_batches)
]
this_validation_loss = np.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
if this_validation_loss < best_validation_loss:
if (this_validation_loss <
best_validation_loss * improvement_threshold):
patience = max(patience, iter * patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [
test_model(i) for i in range(n_test_batches)
]
test_score = np.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print(('Optimization complete. Best validation score of %f %% '
'obtained at iteration %i, with test performance %f %%') %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.1fs' % ((end_time - start_time)))